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---
language:
- en
pretty_name: CTD Pretrain Corpus (v1, capped)
tags:
- pretraining
- clinical-trials
- fda
- pmc
- sql
- python
- text-generation
configs:
- config_name: clinical_trials
data_files: clinical_trials.jsonl
- config_name: generic
data_files: generic.jsonl
- config_name: python
data_files: python.jsonl
- config_name: sql
data_files: sql.jsonl
- config_name: fda
data_files: fda.jsonl
- config_name: pmc
data_files: pmc.jsonl
- config_name: generators_t0_llm
data_files: t0_llm.jsonl
- config_name: generators_t1
data_files: t1.jsonl
- config_name: generators_t2_dedup
data_files: t2_dedup.jsonl
- config_name: generators_t3_q_dedup
data_files: t3_q_dedup.jsonl
- config_name: generators_t4_kpis_v2_relations
data_files: t4_kpis_v2_relations.jsonl
- config_name: generators_t5_templated
data_files: t5_templated.jsonl
- config_name: generators_t7
data_files: t7.jsonl
---
# CTD Pretrain Corpus — v1 (capped)
Pretraining corpus assembled from clinical-trial, regulatory, biomedical-literature,
code, SQL, and synthetic-generator sources. Every record is a JSON object with a
`text` field (plus source-specific `metadata`/`source` fields).
This is the **v1 / capped** release: each source category was streamed in file
order and truncated once its configured token budget was reached (whole-record
cutoff — the record that crosses the limit is kept, so totals land at or just
over the stated cap). Categories with no configured limit (`generators`) are
included in full. The uncapped/full version of every category lives in the
sibling `processed-all` release.
Token counts were computed with the local tokenizer at
`overall/v1/qwen3.5-0.8b` (Qwen 3.5 0.8B tokenizer), counting the `text` field
only, with `add_special_tokens=False`.
## Overall stats
| Category | Tokens | Records | Token limit | Limit reached | Files |
|---|---|---|---|---|---|
| clinical_trials | 1,000,000,017 | 222,238 | 1,000,000,000 | yes | `clinical_trials.jsonl` |
| generators | 892,358,213 | 705,472 | — (no limit) | no | `t0_llm.jsonl`, `t1.jsonl`, `t2_dedup.jsonl`, `t3_q_dedup.jsonl`, `t4_kpis_v2_relations.jsonl`, `t5_templated.jsonl`, `t7.jsonl` |
| generic | 500,000,092 | 725,848 | 500,000,000 | yes | `generic.jsonl` |
| python | 400,000,261 | 486,395 | 400,000,000 | yes | `python.jsonl` |
| sql | 400,009,402 | 54,065 | 400,000,000 | yes | `sql.jsonl` |
| fda | 150,000,137 | 1,694,490 | 150,000,000 | yes | `fda.jsonl` |
| pmc | 300,022,934 | 29,776 | 300,000,000 | yes | `pmc.jsonl` |
| **Total** | **3,642,391,056** | **3,918,284** | | | |
## Generators — per-file breakdown
The `generators` category has no token cap, so every source file is included
in full. Individual file stats:
| File | Records | Tokens |
|---|---|---|
| `t0_llm.jsonl` | 12,473 | 13,544,109 |
| `t1.jsonl` | 637,765 | 765,998,078 |
| `t2_dedup.jsonl` | 30,998 | 6,123,972 |
| `t3_q_dedup.jsonl` | 9,839 | 92,449,908 |
| `t4_kpis_v2_relations.jsonl` | 92 | 1,542,592 |
| `t5_templated.jsonl` | 14,184 | 12,266,811 |
| `t7.jsonl` | 121 | 432,743 |
| **Subtotal (generators)** | **705,472** | **892,358,213** |
## Source categories
- **clinical_trials** — clinical trial records (from `ct-v1/ct-v1.jsonl`), single merged file.
- **generators** — synthetic/templated data-generation outputs (`t0`–`t7` pipeline stages), kept as individual files, no cap applied.
- **generic** — general-domain text sampled from a ~750M-token pool (`generic/all-750M/*.parquet`), merged into one file.
- **python** — Python/code + visualization datasets (`python/*.parquet`), merged into one file.
- **sql** — SQL Q&A / schema data (`sql/*.arrow`), merged into one file.
- **fda** — FDA regulatory pretraining text (`fda_pretrain_full.jsonl`).
- **pmc** — PubMed Central biomedical literature (`pmc.jsonl`).
## Format
Each file is newline-delimited JSON (`.jsonl`). Every record contains at least:
```json
{"text": "...", "metadata": {...}}
```
`metadata`/`source` fields vary by category and reflect the original upstream schema.
## Tokenizer
- Path: `overall/v1/qwen3.5-0.8b`
- Counting method: `Tokenizer.encode_batch(texts, add_special_tokens=False)`, token count = `len(encoding.ids)` per record's `text` field.
## Files in this release
See `stats.json` / `stats.md` in this directory for the machine-readable and
tabular versions of the stats above.